Introduces statistical tools for the analysis of time-dependent data. Data analysis and application will be an integral part of this course. Available methodologies, comparing individual methodologies, selecting a methodology, and designing a forecasting system that fits the specific management. Time series decomposition, regression method, smoothing techniques, moving average (MA), autoregressive (AR) and autoregressive integrated moving average (ARIMA) models, Box-Jenkins methods, Input-Output, and Econometric Models.
Prerequisite(s)
IE3101 Introduction to Probability
Corequisite(s)
-
Special Requisite(s)
-
Instructor(s)
Professor Murat Ermiş
Course Assistant(s)
Schedule
This course is not offered in this semester.
Office Hour(s)
This course is not offered in this semester.
Teaching Methods and Techniques
Oral presentation, Question-answer, Problem solving
Principle Sources
• Montgomery D.C., Jennings, C.L., Kulahci, M. (2008). Introduction to Time Series Analysis and Forecasting, John Wiley.
Other Sources
• Box, George E. P., Jenkins Gwilym M., and Reinsel, Gregory C. (2008). Time Series Analysis: Forecasting and Control, 4th Edition, Wiley.
• B.L., Bowerman, R. O’Connel, and A. Koehler (2004). Forecasting, Time Series, and Regression, Duxbury Applied Series, 4th Edition, Duxbury Applied Series.
• Makridakis, S., Wheelwright, S.C., and Hyndman, R.J. (1998). Forecasting Methods and Applications, 3rd ed., John Wiley.
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction to forecasting and time series
Oral presentation
2. Week
Numerical description of data
Oral presentation
3. Week
Simple Linear Regression
Oral presentation
4. Week
Multiple Linear Regression
Oral presentation
5. Week
Prediction, variable selection, autocorrelation Durbin Watson test
Oral presentation
6. Week
Forecasting: Time series regression
Oral presentation
7. Week
Exponential Smoothing, Holt-Winter methods
Oral presentation
8. Week
Midterm Exam
Oral presentation
9. Week
ARIMA models
Oral presentation
10. Week
AR(p), MA(q) models, Box Jenkins methodology
Oral presentation
11. Week
ARIMA applications
Oral presentation
12. Week
Seasonal ARIMA modeling
Oral presentation
13. Week
Advanced topics in forecasting
Oral presentation
14. Week
Project presentation
Case study
15. Week
Final Exam
16. Week
Final Exam
17. Week
Final Exam
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
25
Homework / Term Projects / Presentations
5
10
Project(s)
1
25
Final Exam
1
40
Program Outcomes
PO-1
Ability to apply theoretical and practical knowledge gained by Mathematics, Science and their engineering fields and ability to use their knowledge in solving complex engineering problems.
PO-2
Ability of determining, defining, formulating and solving complex engineering problems; for that purpose develop the ability of selecting and implementing suitable models and methods of analysis.
PO-3
Ability of designing a complex system, process, device or product under real world constraints and conditions serving certain needs; for this purpose ability of applying modern design techniques
PO-4
Ability of selecting and using the modern techniques and devices which are necessary for analyzing and solving complex problems in engineering implementations; ability of efficient usage of information technologies.
PO-5
Ability of designing experiments, conducting tests, collecting data and analyzing and interpreting the solutions to investigate of complex engineering problems or discipline-specific research topics.
PO-6
Ability of working efficiently in intra-disciplinary and multi-disciplinary teams; individual working ability and habits.
PO-7
Ability of verbal and written communication skills; and at least one foreign language skills, ability to write effective reports and understand written reports, ability to prepare design and production reports, ability to make impressive presentation, ability to give and receive clear and understandable instructions
PO-8
Awareness of importance of lifelong learning; ability to access data, to follow up the recent innovation in science and technology for continuous self-improvement.
PO-9
Conformity to ethical principles; knowledge about occupational and ethical responsibility, and standards used in engineering applications.
PO-10
Knowledge about work life implementations such as project management, risk management and change management; awareness about entrepreneurship and innovativeness; knowledge about sustainable development.
PO-11
Knowledge about effects of engineering applications on health, environment and security in global and social dimensions, and on the problems of the modern age in engineering; awareness about legal outcomes of engineering solutions.
Learning Outcomes
LO-1
Ability to collect data, analyze data, interpret and present the results.
LO-2
Ability to use classical forecasting techniques.
LO-3
Implement moving averages, exponential smoothing, and time-series decomposition.
LO-4
Implement simple and multiple regression analysis for forecasting.
LO-5
Ability to use the Box-Jenkins (ARIMA) method.
LO-6
Use Minitab and Excel software to apply the concepts learned to practical applications of real-life problems.
